rmserr: Accuracy Measures

In pracma: Practical Numerical Math Functions

Accuracy Measures

Description

Calculates different accuracy measures, most prominently RMSE.

Usage

rmserr(x, y, summary = FALSE)

Arguments

x, y	two vectors of real numbers
summary	logical; should a summary be printed to the screen?

Details

Calculates six different measures of accuracy for two given vectors or sequences of real numbers:

MAE	Mean Absolute Error
MSE	Mean Squared Error
RMSE	Root Mean Squared Error
MAPE	Mean Absolute Percentage Error
LMSE	Normalized Mean Squared Error
rSTD	relative Standard Deviation

Value

Returns a list with different accuracy measures.

Note

Often used in Data Mining for predicting the accuracy of predictions.

References

Gentle, J. E. (2009). Computational Statistics, section 10.3. Springer Science+Business Media LCC, New York.

Examples

x <- rep(1, 10)
y <- rnorm(10, 1, 0.1)
rmserr(x, y, summary = TRUE)

pracma documentation built on March 19, 2024, 3:05 a.m.